Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion
Ensuring a sustainable global food security status which necessitated by achieving an equilibrium state between the anticipated and significant rise in the global population and the projected agricultural output which is essential for their food adequacy. The absence of such a harmonious balance may...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402500698X |
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| author | Asif Raza Abdul Hammed Pitafi M. Kahsif Shaikh Khaliq Ahmed |
| author_facet | Asif Raza Abdul Hammed Pitafi M. Kahsif Shaikh Khaliq Ahmed |
| author_sort | Asif Raza |
| collection | DOAJ |
| description | Ensuring a sustainable global food security status which necessitated by achieving an equilibrium state between the anticipated and significant rise in the global population and the projected agricultural output which is essential for their food adequacy. The absence of such a harmonious balance may be a contributing factor to the emergence of food crises worldwide. Hence, it is imperative to proactively address and mitigate both direct and indirect factors that could potentially lead to this agricultural yield imbalance. Facilitating optimal plant growth and implementing effective measures against diseases play a fundamental role in meeting the global demand for food in terms of both quality and quantity. This article offered a hybrid model based on Deep learning called DENSE-NET-121 with 2D Gaussian elimination filters that can be effective deep learning tools to increase potato yield by early detection of the leaf. Three types of potato leaf classes called Early Blight, Healthy, and Late Blight are incorporated by Dataset which has been taken from the kaggle repository. Considering this proposed model, state-of-the-art DENSE-NET-121 has produced an unprecedented training and validation accuracy 0.9908, 0.9837 respectively furthermore model also produced extremely low training and validation loss 0.0683, 0.0796 and an error rate below then 0.1 as well. Furthermore model produced average Precision, and recall, 0.98, 0.96, and 0.97 respectively. |
| format | Article |
| id | doaj-art-3b17a63f788c45bdbba4d028bfbf2f93 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-3b17a63f788c45bdbba4d028bfbf2f932025-02-05T04:32:17ZengElsevierHeliyon2405-84402025-02-01113e42318Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusionAsif Raza0Abdul Hammed Pitafi1M. Kahsif Shaikh2Khaliq Ahmed3Universiti Kuala Lumpur, Malaysian Institute of Information Technology (UniKL MIIT), 1016, Jalan Sultan Ismail, 50250, Kuala Lumpur, Malaysia; Department of Computer Science and Information Technology, Sir Syed University of Engineering & Technology, Karachi, PakistanDepartment of Computer Science and Information Technology, Sir Syed University of Engineering & Technology, Karachi, Pakistan; Corresponding author.Department of Software Engineering, Sir Syed University of Engineering & Technology, Karachi, PakistanDepartment of Computer Science, Iqra University, Karachi, PakistanEnsuring a sustainable global food security status which necessitated by achieving an equilibrium state between the anticipated and significant rise in the global population and the projected agricultural output which is essential for their food adequacy. The absence of such a harmonious balance may be a contributing factor to the emergence of food crises worldwide. Hence, it is imperative to proactively address and mitigate both direct and indirect factors that could potentially lead to this agricultural yield imbalance. Facilitating optimal plant growth and implementing effective measures against diseases play a fundamental role in meeting the global demand for food in terms of both quality and quantity. This article offered a hybrid model based on Deep learning called DENSE-NET-121 with 2D Gaussian elimination filters that can be effective deep learning tools to increase potato yield by early detection of the leaf. Three types of potato leaf classes called Early Blight, Healthy, and Late Blight are incorporated by Dataset which has been taken from the kaggle repository. Considering this proposed model, state-of-the-art DENSE-NET-121 has produced an unprecedented training and validation accuracy 0.9908, 0.9837 respectively furthermore model also produced extremely low training and validation loss 0.0683, 0.0796 and an error rate below then 0.1 as well. Furthermore model produced average Precision, and recall, 0.98, 0.96, and 0.97 respectively.http://www.sciencedirect.com/science/article/pii/S240584402500698XMachine learningPotato leafDENSE-NET 121Deep learning2D Gaussian filter |
| spellingShingle | Asif Raza Abdul Hammed Pitafi M. Kahsif Shaikh Khaliq Ahmed Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion Heliyon Machine learning Potato leaf DENSE-NET 121 Deep learning 2D Gaussian filter |
| title | Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion |
| title_full | Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion |
| title_fullStr | Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion |
| title_full_unstemmed | Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion |
| title_short | Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion |
| title_sort | optimizing potato leaf disease recognition insights dense net 121 and gaussian elimination filter fusion |
| topic | Machine learning Potato leaf DENSE-NET 121 Deep learning 2D Gaussian filter |
| url | http://www.sciencedirect.com/science/article/pii/S240584402500698X |
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